Most genetic variants associated with complex traits are hypothesized to regulate gene expression. To understand the genetics underlying gene expression variability, we characterized 14,324 RNA-sequencing samples from the Trans-Omics for Precision Medicine program and performed expression and splicing quantitative trait locus (e/sQTL) analyses in six tissues and cell types, including whole blood (n = 6454) and lung (n = 1291). We detected tens of thousands of secondary cis-e/sQTLs, showing that secondary cis-e/sQTL discovery remains unsaturated. We fine-mapped UK Biobank-derived genome-wide association study (GWAS) signals from 164 traits and identified e/sQTL colocalizations for 10,611 GWAS signals, including 7096 that colocalize with secondary e/sQTLs. Our results suggest that even larger e/sQTL analyses will uncover additional secondary e/sQTLs, further benefiting GWAS interpretation.

Severe asthma is a chronic disease of airway inflammation with substantial morbidity. Deficient specialized pro-resolving mediators (SPMs) are associated with persistent airway inflammation and impaired lung function in some patients with severe asthma. Resolvin D1 (RvD1) is an SPM agonist for inflammation resolution. Plasma RvD1 was measured longitudinally over 5 years in 23 severe asthma patients in the Severe Asthma Research Program (SARP) to identify relationships between clinical parameters, type 2 inflammation, and sputum gene expression. The majority of severe asthma patients had persistently low plasma RvD1; a smaller subgroup had higher RvD1 that fluctuated over time. Correlation analysis indicated a relationship between plasma RvD1 and sputum eosinophilia. A subgroup of severe asthma patients had low plasma RvD1 and high sputum eosinophils (RvD1LoSpEosHi); a separate subgroup had high plasma RvD1 and low sputum eosinophils (RvD1HiSpEosLo). The RvD1LoSpEosHi patient cluster had increased T2 inflammation, lower lung function, and more asthma exacerbations. 42 genes were differentially expressed in RvD1LoSpEosHi severe asthma sputum, including hypoxia-inducible factor 1-alpha (HIF1A). RvD1 significantly downregulated eosinophil HIF-1α expression in vitro. These findings identify a subset of severe asthma patients with low RvD1 and increased sputum eosinophilia, and RvD1 regulation of eosinophil activation ex vivo, suggesting a counter-regulatory role for this SPM in modulating eosinophilic T2 inflammation in asthma.

Background Studies comparing individuals with FEV1%predicted ≥80% but FEV1/FVC <0.7 to those with both FEV1%predicted ≥80% and FEV1/FVC ≥0.7 have reported inconsistent results. However, these studies were limited by the use of a fixed FEV1/FVC threshold and the absence of adjustment for baseline FEV1. Given that individuals with preserved FEV1 but airflow limitation (ALp) often have lower FEV1 than those with normal spirometry, this imbalance may have influenced the observed outcomes. Research Question Is airflow limitation in the context of preserved FEV1 a clinically significant spirometry pattern? Study Design and Methods We aimed to assess the association of FEV1/FVC< lower limit of normal (LLN) with clinical, functional, and radiographic features, and outcomes in individuals with preserved FEV1, while accounting for baseline FEV1. Using COPDGene data, we categorized ever-smokers with post-bronchodilator FEV1 ≥ LLN into 2 groups based on FEV1/FVC: Normal and ALp (airflow limitation). We applied multiple statistical models adjusting for demographics, smoking history, and baseline FEV1. Analyses were replicated in the SPIROMICS cohort. Results Of the 6,067 participants with normal FEV1, 5,192 had normal spirometry, and 875 had ALp. Compared to normal spirometry, ALp was associated with chronic bronchitis (OR=1.65; 95%CI: 1.35 to 2.01; P< 0.001), greater CT-assessed emphysema, gas trapping, and functional small airway disease. ALp was associated with an additional FEV1 decline of 9.3 mL/year (95% CI: 6.5 to 12.0; P < 0.001), more respiratory exacerbations (incidence rate ratio=1.50; 95%CI: 1.26 to 1.81; P<0.001) but not increased mortality compared to normal spirometry. Findings were consistent in SPIROMICS. Interpretation Airflow Limitation with Preserved FEV1 in individuals with cigarette smoking history represents a clinically meaningful obstructive phenotype, that warrants recognition in clinical practice.

Accurately measuring medication exposure over time is essential for clinical research, adverse-event monitoring, and treatment optimization. However, this is difficult when utilizing electronic health record (EHR) data, especially when prescriptions include variable dosing instructions or frequent dose changes [1]. Glucocorticoid (GC) use in inflammatory disease exemplifies this challenge. GCs are typically initiated at higher doses with a gradual taper and may be altered based on disease response [2, 3]. Additionally, cumulative exposure is clinically important because of its association with substantial metabolic and infectious side effects. [4, 5] While natural language processing (NLP) approaches have been used to extract medication information, many existing methods focus on identifying medications or extracting single-dose information rather than reconstructing dose changes over time [6, 7]. This study aimed to develop and validate a large language model (LLM)–assisted NLP pipeline for extracting GC tapering instructions from semi-structured EHR prescription data and calculating longitudinal Patients with International Classification of Diseases, Ninth or Tenth Revision diagnosis codes for giant cell arteritis and/or polymyalgia rheumatica were included if GC prescriptions covered at least 75% of days within a 12-month follow-up period. Prescription data were preprocessed using BigQuery SQL. Gemini-2.5-Flash was prompted using a few-shot strategy to extract tapering instructions, and a Python-based deterministic parsing module converted model outputs into structured daily dose values. The pipeline calculated 365-day cumulative GC exposure, which was compared with manually reviewed reference doses. Performance was evaluated using correlation, classification metrics, Cohen κ, and absolute percentage error.cumulative GC exposure. Patients with International Classification of Diseases, Ninth or Tenth Revision diagnosis codes for giant cell arteritis and/or polymyalgia rheumatica were included if GC prescriptions covered at least 75% of days within a 12-month follow-up period. Prescription data were preprocessed using BigQuery SQL. Gemini-2.5-Flash was prompted using a few-shot strategy to extract tapering instructions, and a Python-based deterministic parsing module converted model outputs into structured daily dose values. The pipeline calculated 365-day cumulative GC exposure, which was compared with manually reviewed reference doses. Performance was evaluated using correlation, classification metrics, Cohen κ, and absolute percentage error. After filtering adequate prescription coverage, 100 patients were included in the final analysis. Pipeline-derived cumulative 365-day doses correlated strongly with manually reviewed reference doses (r=0.84; P<.001). When cumulative doses were categorized into low-, medium-, and high-dose groups, the model achieved 80% accuracy, 85% precision, 73% recall, an F1 score of 76%, and an area under the receiver operating characteristic curve of 94% for identifying the highest-risk dose category. The mean individual-level absolute percentage error was 23%, while cohort-level error was 7%. In prescription-level validation, 100 records were annotated for accuracy in dose extraction. The model achieved an accuracy of 96%, a macro F1 score of 97%, and a quadratic-weighted Cohen Kappa of 94% (un-weighted: 93%). An LLM–assisted NLP pipeline combined with deterministic parsing can extract complex GC tapering instructions from semi-structured EHR prescription data and estimate longitudinal cumulative medication exposure. This approach offers a scalable solution for medication exposure assessment in clinical research cohorts, particularly when manual review is not feasible. Further validation is needed across other medication classes, clinical settings, and EHR systems. N/A

BACKGROUNDWe constructed multi-trait polygenic risk scores (PRSs) predicting chronic obstructive pulmonary disease (COPD) and exacerbations, validated their performance in diverse cohorts, and identified PRS-related proteins for potential therapeutic targeting.METHODSPRSmix+, a multi-trait PRS framework, is used to train a composite PRS (PRSmulti) in COPDGene non-Hispanic White participants (n = 6,647). Associations of PRSmulti with COPD status (GOLD 2-4 vs. GOLD 0 or ICD) and exacerbation frequency were tested in COPDGene African American (n = 2,466), ECLIPSE (n = 1,858), Mass General Brigham Biobank (n = 15,152), and All of Us (n = 118,566). Protein prediction models were applied to GWAS summary statistics from traits contributing to PRSmulti and were validated with proteomic data in COPDGene (n = 5,173) and UK Biobank (n = 5,012).RESULTSPRSmix+ selected 7 traits for PRSmulti. In multivariable models, PRSmulti was associated with COPD status (meta-analysis random effects [RE] OR 1.58 [95% CI: 1.28-1.94]) and exacerbation frequency (meta-analysis RE β 0.21 [95% CI: 0.11-0.31]), with higher effect sizes observed in smoking-enriched cohorts. PRSmulti outperformed traditional single-trait PRS in all tested cohorts. Using protein prediction models, we identified 73 proteins associated with the PRSs that were also validated with measured protein levels in COPDGene and UK Biobank. Of these proteins, 25 were linked to approved or investigational drugs. Notable targets include RAGE/sRAGE, IL1RL1, and SCARF2, all implicated in COPD pathogenesis and exacerbations.CONCLUSIONSMulti-trait PRS improves prediction of COPD and exacerbation risk. Integration with proteomic data identifies druggable protein targets, offering a promising avenue for precision medicine in COPD management.TRIAL REGISTRATIONCOPDGene: ClinicalTrials.gov NCT00608764; ECLIPSE: ClinicalTrials.gov NCT00292552.

OBJECTIVE:To investigate the relationship between depressive symptoms and risk for COVID-19 hospitalization and death in a United States general population-based sample. METHODS:We studied participants enrolled into observational NIH-funded cohorts from 1971-2010 with ongoing follow-up through 2023. Pre-pandemic Centers for Epidemiologic Studies Depression (CES-D) 10-item scale scores ≥10 defined elevated depressive symptoms. Questionnaires, medical records, and death certificates classified incident COVID-19 as severe (hospitalized/fatal) or non-severe from April 2020 through February 2023. RESULTS:Of 33,565 participants, 633 (1.9%) had incident severe COVID-19 over a mean (SD) follow-up of 453 (207) days. Elevated pre-pandemic depressive symptoms were present in 4,922 (16.3%) of participants. Elevated pre-pandemic depressive symptoms increased the hazard of severe COVID-19 in fully-adjusted (aHR=1.27; 95%CI: 1.03-1.57) models. In sex-stratified models (p-interaction=0.03), elevated depressive symptoms increased the hazard in women (aHR=1.49; 95%CI: 1.17-1.90) but not in men (aHR=0.96; 95%CI: 0.67-1.39). CONCLUSIONS:Pre-pandemic depressive symptoms were associated with increased risk of severe COVID-19 among women in a large US general population-based study of adults, and associations in men were neither confirmed nor ruled out. These results support current CDC recommendations that depression be considered an underlying medical condition associated with higher risk for severe COVID-19 and suggest that increased clinical focus on risk mitigation for COVID-19 and other acute respiratory infections among patients with depressive symptoms is warranted.

RATIONALE:Idiopathic Pulmonary Fibrosis (IPF) is characterized by chronic progressive pulmonary fibrosis and high mortality. Genetic markers, summarized into a polygenic risk score (PRS), associate with IPF in well-phenotyped research cohorts. OBJECTIVES:To evaluate the performance of the PRS using real-world data from routinely captured electronic healthcare records. METHODS:We conducted an observational study evaluating the association of a PRS for IPF with electronic healthcare record diagnosis of IPF as well as lung transplant-free survival in four independent cohorts; the Mass General Brigham Biobank (MGBB), Mayo Clinic Biobank (MCBB), Mayo Clinic Tapestry Cohort (Tapestry), and U.K. Biobank (UKBB). We used multivariable logistic regression and multivariable Cox proportional hazards models adjusting for age, gender, and principal components of ancestry. The cohorts then underwent fixed and random effects meta-analysis. MEASUREMENTS AND MAIN RESULTS:Of 37,709; 44,195; 43,202; and 447,422 participants from MGBB, MCBB, Tapestry and UKBB respectively, 1,015 (2.7%) 2,879 (6.5%), 1,310 (3.0%), and 2,742 (0.6%) participants had an IPF diagnosis. Meta-analysis demonstrated a high-risk PRS associated with IPF diagnosis, OR 2.88 (95%CI 2.41-3.44) compared to all other individuals. A high-risk PRS also associated with the composite endpoint of mortality or lung transplant among those with an IPF diagnosis, HR 1.23(95%CI 1.11-1.35) compared to all other individuals. CONCLUSIONS:A PRS can identify those at risk for an IPF diagnosis and mortality in biobank-scale data, which may have implications for clinical decisions. Further work is necessary to evaluate the utility of adding genetics in clinical settings.

ABSTRACT Background Chronic obstructive pulmonary disease (COPD) is associated with musculoskeletal comorbidities, including cachexia. Weight loss (WL) is the major criterion for cachexia and increases risk for mortality in COPD. Risk factors for WL in COPD are incompletely understood. We performed this whole genome sequencing (WGS) analysis to identify genetic risk variants for WL in COPD. Methods We studied 16 972 participants from the Trans‐Omics for Precision Medicine (TOPMed) Initiative and All of Us Research Program. COPD was diagnosed using spirometry in TOPMed, while diagnosis codes were used in All of Us. WL was defined as WL ≥ 5% or a final body mass index (BMI) < 20 kg/m2. WGS data came from white blood cells in all cohorts. Single‐variant testing was conducted on both race‐ and study‐stratified cohorts and in a cosmopolitan, ancestry‐independent manner using GENESIS in TOPMed and SAIGE in All of Us. SAIGE‐GENE+ gene‐based analyses were performed on race‐stratified and cosmopolitan cohorts. Single variant meta‐analyses were conducted using METAL within (B/AA and NHW analyses of Black/African–American and non‐Hispanic white participants, respectively) and across racial groups (COSMO). Rare variant gene‐based results were combined using Fisher's method. Transcriptomic effects were predicted using MetaXcan. We used the GWAS Catalogue to analyse for colocalization with other related traits. Results Two single variants were associated with WL in COPD among All of Us participants: one intronic variant in HCN1 in Black/African–American participants (chr5:45271359:TACACAC:T, odds ratio with 95% confidence interval (OR (CI95)) = 2.43(1.78–3.31), p = 1.95 × 10−8) and one intergenic variant between PPP4R2 and PDZRN3 in the cosmopolitan and NHW cohorts (chr3:73345901:A:G, OR (CI95) = 0.21(0.12–0.35), p = 8.84 × 10−9 in cosmopolitan and OR (CI95) = 0.18(0.10–0.33), p = 9.44 × 10−9 in NHW). Single‐variant meta‐analysis identified two loci associated with WL in COPD: five variants within DRAIC in the B/AA meta‐analysis (lead variant chr15:69571341:A:G, OR (CI95) = 1.37(1.23–1.51), p = 1.29 × 10−9) and two intronic variants within RFX3 in the cosmopolitan meta‐analysis (lead variant chr9:3390983:T:C, OR (CI95) = 1.50(1.31–1.73), p = 1.06 × 10−8). Rare variants within RNU6‐565P (NHW analysis in All of Us; p = 2.83 × 10−7) and LOC339298 (B/AA analysis in All of Us, p = 1.85 × 10−6) were associated with WL in COPD. The RNU‐565P signal remained significant after combination with TOPMed results (pcombined = 1.23 × 10−6). MetaXcan predicted differential expression of LNC00959 in visceral adipose tissue (p = 1.16 × 10−6 in COSMO analysis). Colocalization analyses identified genomic associations between BMI and variants in or near DRAIC, RFX3, PDZRN3, and LINC00959. Conclusions In the first WGS analysis of WL in COPD, we have identified seven novel loci. Further characterization of these loci will validate our findings and improve our understanding of the molecular pathophysiology of this condition.

Background:Sex and age have long been known to affect lung function. Several biological variables and anatomical factors may contribute to sex- and age-related differences in pulmonary metrics. Objective:We hypothesized that a machine learning model could be trained to predict a person's lung age and self-reported sex using pulmonary function test data. Methods:We retrospectively analyzed complete pulmonary function tests from 6392 healthy adults across 3 Mayo Clinic regions. Four models of increasing complexity were trained using gradient-boosted machines to predict chronological age and biological sex. Model interpretability was assessed using Shapley additive explanation values and partial dependence plots. Quantile regression was used to estimate reference percentiles for predicted lung age. Results:The best-performing age model (model 4, inclusive of time-series features) achieved a root mean square error of 7.01 years (95% CI 6.73-7.30) and a mean absolute error of 5.55 years (95% CI 5.32-5.80). The best-performing sex classification model (model 4) achieved an area under the curve of 0.981 (95% CI 0.975-0.988), sensitivity of 91.7% (95% CI 89.0%-93.9%), and specificity of 95.6% (95% CI 93.9%-97%). Key predictors for lung age included residual volume as a percentage of total lung capacity (TLC), forced expiratory volume in 1 second, and alveolar volume. For sex classification, peak expiratory flow, height, and age were among the most influential features. Age-stratified evaluation showed the overestimation of lung age in younger adults and underestimation in older adults. Predicted lung age increased broadly with chronological age, and quantile regression provided normative reference ranges. Conclusions:Applying artificial intelligence to pulmonary function data allows the prediction of a patient's sex and estimation of lung age. The ability of an artificial intelligence algorithm to determine physiological lung age, with further validation, may serve as a measure of overall respiratory health.

BACKGROUND:Asthma is a heterogeneous disease influenced by genetic and environmental factors. Fine particulate matter (PM2.5) exacerbates asthma, likely through oxidative stress pathways, but whether genetic variation modifies this effect remains unclear. METHODS:We analysed data on 948 adults with asthma from the Severe Asthma Research Program (SARP), linking ZIP-code-level PM2.5 exposure with whole-genome sequencing data. We tested 4337 single nucleotide polymorphisms (SNPs) in 120 oxidative stress pathway genes for gene-environment (GxE) interactions with PM2.5 on lung function (forced expiratory volume in 1 s [FEV1] % predicted) using weighted linear regression. Gene expression data from bronchial epithelial cells (n = 170) were used to assess cis-expression quantitative trait loci (eQTLs). FINDINGS:Higher PM2.5 exposure was associated with lower FEV1% predicted (β per μg/m3 = -0.7, p = 0.01). We identified 20 SNPs across seven genes (OXSR1, PXDN, TPO, LRRK2, APP, MSRA, MSRB2) with significant GxE interactions after multiple-testing correction. Five SNPs were also eQTLs, linking PM2.5-modified gene expression to lung function. Minor alleles in OXSR1 and PXDN were associated with reduced gene expression and worsened FEV1% under high PM2.5 exposure. Conversely, TPO variants were associated with higher baseline expression and lower lung function, but under increasing PM2.5 exposure, minor allele carriers showed suppressed TPO expression and improved FEV1%. INTERPRETATION:This study identified 20 SNPs in oxidative stress pathway genes that modify the effect of PM2.5 on lung function in asthma. These findings highlight the importance of integrating environmental context in genetic studies and suggest potential therapeutic targets for pollution-sensitive asthma phenotypes. FUNDING:Supported by NIH grants.

OBJECTIVE:To evaluate the impact of genetic testing and telomere analysis among patients with fibrotic interstitial lung disease, we established a translational genetic testing and counseling unit for patients referred from our pulmonary practice. PATIENTS AND METHODS:We included patients referred to the genetic testing and counseling unit between 2019 and 2023 who presented either with a family history of pulmonary fibrosis, progressive fibrotic disease, clinical indicators of surfactant protein or telomere biology disorders (eg, early greying of hair, etc), or early-onset fibrosis (≤60 years). Patients underwent a pulmonary fibrosis-multigene sequencing panel and telomere length assessment. Clinical data, genetic sequencing results, and telomere measurements were collected and analyzed. RESULTS:Of 66 referred patients, 54 (82%) completed genetic testing. Common clinical diagnoses were unclassifiable fibrotic lung disease (29%) and idiopathic pulmonary fibrosis (26%). The predominant radiologic pattern was indeterminate for usual interstitial pneumonia (42%). Telomere lengths, measured in 47 patients (71%), were less than or equal to the 10th percentile in lymphocytes and/or granulocytes for age-matched controls in 37 patients (79%). Pathogenic/likely pathogenic variants were identified in 10 patients (19%), predominantly in telomere pathway genes (9 of 10 cases). Shorter lymphocyte telomere length increased odds of identifying a genetic etiology (odds ratio: 6.26; 95% CI: 1.50 to 33.83; P = .01). Importantly, genetic findings impacted clinical decisions in more than half of tested patients. CONCLUSION:Comprehensive genetic sequencing combined with telomere length analysis enhances diagnostic accuracy in fibrotic interstitial lung disease, identifying a high proportion of cases attributable to telomere disorders. Our integrated clinical model demonstrates significant translational value, influencing patient management, and therapeutic decision-making.

Chronic obstructive pulmonary disease (COPD) exhibits marked heterogeneity in lung function decline, mortality, exacerbations, and other disease-related outcomes. Omic risk scores (ORS) estimate the cumulative contribution of omics, such as the transcriptome, proteome, and metabolome, to a particular trait. This study evaluated associations between blood-based ORS and COPD-related traits in both smoking-enriched and general population cohorts. ORS were developed and tested in 3,339 participants of Genetic Epidemiology of COPD (COPDGene) with blood RNA-sequencing, proteomic, and metabolomic data. Single- and multi-omic risk scores were trained on 24 cross-sectional and five longitudinal traits using 80

Rationale:Nebulizers are an alternative to handheld devices for inhaled therapies in chronic obstructive pulmonary disease (COPD). Understanding nebulizer utilization patterns is essential to developing therapy guidelines. Objectives:We aimed to describe characteristics of nebulizer users versus nonusers and factors associated with baseline nebulizer use and longitudinal uptake. Methods:We analyzed the Subpopulations and Intermediate Outcome Measures in COPD Study, a prospective cohort of 2973 participants with or without tobacco use and/or COPD. We used cross-sectional multivariable logistic regression and interval-censored proportional hazard models to analyze factors associated with nebulizer use and uptake among tobacco-exposed participants with preserved spirometry (TEPS) and COPD from enrollment (Visit 1) through 4–7 years of follow-up (Visit 5). Results:Nebulizer utilization was highest in advanced COPD, 49% of Global initiative for chronic Obstructive Lung Disease (GOLD) Group D participants at baseline. Nebulizer treatments were primarily as-needed short-acting bronchodilators. Baseline nebulizer use was associated with respiratory exacerbations in the prior year (1, odds ratio [OR] 1.81, 95% confidence interval [CI] [1.24, 2.64]; 2, OR 1.86, 95% CI [1.07, 3.22]; 3 or more, OR 1.87, 95% CI [1.07, 3.28]), lower forced expiratory volume in 1 second (FEV1) (OR 2.81 per liter decrease, 95% CI [2.09, 3.77]), COPD Assessment Test (CAT) score >10 (OR 1.89, 95% CI [1.17, 3.03]), 6-minute walk distance (6MWD) (OR 1.03 per 10 meter lower 6MWD, 95% CI [1.02, 1.05]), and a history of asthma (OR 2.41, 95% CI [1.76, 3.30]). Longitudinal uptake was similarly associated with exacerbations, lower FEV1, CAT score >10, and asthma. Patterns were consistent between TEPS and COPD. Conclusion:Nebulizers were predominantly used by participants with frequent exacerbations, high symptom burden, and advanced COPD, and long-acting nebulized medications were underutilized. Randomized controlled trials are needed to compare nebulizers with hand-held devices.

RATIONALE: Chronic lower respiratory diseases (CLRD) confer higher risks from respiratory viral infections, hence CLRD patients are prioritized for influenza, pneumococcal, and COVID-19 vaccination. Nonetheless, vaccine deferral and declination remain common. Given that COVID-19 vaccination is particularly polarizing, and attitudes toward it may differ from other vaccines, we assessed COVID-19 vaccination behaviors and attitudes in adults with versus without CLRD and/or smoking history, which is strongly associated with CLRD risk. METHODS: We pooled data from 10 cohorts participating in the Collaborative Cohort of Cohorts for COVID-19 Research (C4R) that collected pre-pandemic data on self-reported physician diagnosis of asthma or COPD. C4R assessed COVID-19 vaccination status through three waves of standardized questionnaires (2020-2022, 2021-2023, 2023-2024). Prompt COVID-19 vaccination was defined as self-report of receiving at least one COVID-19 vaccine by May 1, 2021. Cumulative COVID-19 vaccine count was self-reported on the Wave 3 Questionnaire. Vaccine attitudes and receipt of influenza and/or pneumococcal vaccines were self-reported on the Wave 1 or 2 Questionnaire. Age-adjusted Poisson regression was used to test associations of vaccination behaviors with CLRD and smoking status at the most recent pre-pandemic exam. RESULTS: Of 20,034 participants (pre-pandemic COPD, 9.3%; asthma, 11.7%; current smoking, 13.4%; former smoking, 36.3%), 64.2% reported prompt COVID-19 vaccination. Among the subset of 10,626 participants completing the Wave 3 Questionnaire in 2023-24, the median COVID-19 vaccine count was 3 (Q1:2, Q3:4). Compared to participants without CLRD, prompt vaccination was more likely in those with asthma (RR=1.04, 95%CI:1.01-1.07, p=0.008) and less likely with COPD (RR=0.94, 95%CI:0.91-0.98, p=0.001), but there were no differences in doses for asthma and COPD. Compared to never smokers, former smoking participants were more likely to receive prompt vaccination (RR=1.11, 95%CI:1.08-1.13, p<0.0001) and more vaccine doses (RR=1.03, 95%CI:1.01-1.05, p=0.015); conversely, current smoking was inversely associated with prompt vaccination (RR=0.94, 95%CI:0.91-0.97, p=0.001) and was associated with fewer doses (RR=0.93, 95%CI:0.89-0.96, p<0.0001). Participants with CLRD versus without CLRD were more likely to report receiving influenza and pneumococcal vaccines, but they were more likely to believe that vaccines are unsafe or ineffective (figure). CONCLUSIONS: COPD was associated with lower likelihood of prompt COVID-19 vaccination. Current smoking was associated with both lower likelihood of prompt COVID-19 vaccination and a lower number of COVID-19 vaccine doses by 2023-24. These associations may be partly attributed to negative vaccine attitudes and beliefs. How the COVID-19 pandemic may have influenced vaccine behaviors and attitudes merits additional study.

Background: Asthma pathophysiology is associated with mitochondrial dysfunction. Mitochondrial DNA copy number (mtDNA-CN) has been used as a proxy of mitochondrial function, with lower levels indicating mitochondrial dysfunction in population studies of cardiovascular diseases and cancers. Objectives: We investigated whether lower levels of mtDNA-CN are associated with asthma diagnosis, severity, and exacerbations. Methods: mtDNA-CN is evaluated in blood from 2 cohorts: UK Biobank (UKB) (asthma, n = 39,147; no asthma, n = 302,302) and Severe Asthma Research Program (SARP) (asthma, n = 1283; nonsevere asthma, n = 703). Results: Individuals with asthma have lower mtDNA-CN compared to individuals without asthma in UKB (beta,-0.006 [95% confidence interval,-0.008 to-0.003], P = 6.23 x 10-6). Lower mtDNA-CN is associated with asthma prevalence, but not severity in UKB or SARP. mtDNA-CN declines with age but is lower in individuals with asthma than in individuals without asthma at all ages. In a 1-year longitudinal study in SARP, mtDNA-CN was associated with risk of exacerbation; those with highest mtDNA-CN had the lowest risk of exacerbation (odds ratio 0.333 [95% confidence interval, 0.173 to 0.542], P = .001). Biomarkers of inflammation and oxidative stress are higher in individuals with asthma than without asthma, but the lower mtDNA-CN in asthma is independent of general inflammation or oxidative stress. Mendelian randomization studies suggest a potential causal relationship between asthma-associated genetic variants and mtDNA-CN. Conclusion: mtDNA-CN is lower in asthma than in no asthma and is associated with exacerbations. Low mtDNA-CN in asthma is not mediated through inflammation but is associated with a genetic predisposition to asthma. (J Allergy Clin Immunol 2025;155:1224-35.)

Despite the availability of effective vaccines and a recent decrease in annual deaths, COVID-19 remains a leading cause of death. Serological studies provide insights into host immunobiology of adaptive immune response to infection, which holds promise for identifying high-risk individuals for adverse COVID-19 outcomes. We investigated correlates of anti-nucleocapsid antibody responses following SARS-CoV-2 infection in a US population-based meta-cohort of adults participating in longstanding National Institutes of Health-funded cohort studies. Anti-nucleocapsid antibodies were measured from dried blood spots collected between February 2021 and February 2023. Among 1419 Collaborative Cohort of Cohorts for COVID-19 Research participants with prior SARS-CoV-2 infection, the mean age (standard deviation) was 65.8 (12.1), 61% were women, and 42.8% self-reported membership in a race/ethnicity minority group. The proportion of participants reactive to nucleocapsid peaked at 69% by 4 months after infection and waned to only 44% ≥12 months after infection. Higher anti-nucleocapsid antibody response was associated with older age, Hispanic or American Indian Alaskan Native (vs White) race/ethnicity, lower income, lower education, former smoking, and higher anti-spike antibody levels. Asian race (vs White) and vaccination (even after infection) were associated with lower nucleocapsid reactivity. Neither vaccine manufacturer nor common cardiometabolic comorbidities were not associated with anti-nucleocapsid response. These findings inform the underlying immunobiology of adaptive immune response to infection, as well as the potential utility of anti-nucleocapsid antibody response for clinical practice and COVID-19 serosurveillance.

BACKGROUND:α1-Antitrypsin deficiency is caused by rare pathogenic variants in SERPINA1, the strongest genetic risk factor for chronic obstructive pulmonary disease. Few studies have evaluated the effects of SERPINA1 variation on asthma severity accounting for critical gene-by-environment interactions with smoking. OBJECTIVE:To characterize the influence of SERPINA1 variation on asthma severity. METHODS:DNA samples from 847 non-Hispanic White and 446 African American participants from the Severe Asthma Research Program underwent SERPINA1 resequencing to identify rare variants. An independent population of 1955 individuals with asthma and α1-antitrypsin concentrations from a Cleveland Clinic Health System (CCHS) database were evaluated for severity measures. RESULTS:In White participants, a history of minimum smoking significantly interacted with SERPINA1 low-to-rare frequency variation to determine risk for asthma-related health care utilization. This was attributed to protease inhibitor type Z heterozygotes (MZ, N = 11), who had a higher frequency of emergency department (ED) visits (6 [54.5%] MZ heterozygotes, odds ratio [OR] = 7.60, 95% confidence interval [CI] = 1.71-39.7, P = .010), hospitalization (5 [45.5%], OR = 16.1, 95% CI = 2.64-150.4, P = .0050) in the past year, and lifetime intensive care unit (ICU) admissions (6 [54.5%], OR = 12.5, 95% CI = 2.44-75.6, P = .0032) compared with 146 individuals without SERPINA1 variants (30 [20.5%] reporting ED visits, 17 [11.6%] hospitalization, and 15 [10.3%] ICU admission). SERPINA1 variant-by-ever smoking interactions in African American participants for ED visits (P = .069) were related to 4 of 6 compound heterozygotes reporting an ED visit. In CCHS, α1-antitrypsin concentrations were inversely associated with moderate-to-severe asthma risk (OR = 0.97 per 10 mg/dL increase in α1-antitrypsin, 95% CI = 0.94-0.99, P = .010) and exacerbations (OR = 0.84 per 10 mg/dL, 95% CI = 0.76-0.94, P = .002). CONCLUSIONS:SERPINA1 variation and α1-antitrypsin concentrations impact asthma severity through gene-environment interactions with minimum smoking.

Importance Identifying factors associated with resilience during the COVID-19 pandemic can inform targeted interventions and resource allocation for groups disproportionately affected by systemic inequities. Objective To examine factors associated with self-reported resilience during the COVID-19 pandemic in racially and ethnically diverse, community-dwelling US adults. Design, Setting, and Participants This cross-sectional study was conducted as part of the Collaborative Cohort of Cohorts for COVID-19 Research (C4R) study, which assessed the associations of the pandemic with self-reported resilience of participants from 14 established US prospective cohorts since January 2021. This report includes participants who responded to the self-reported resilience question on C4R questionnaires. Data was initially analyzed from October 2023 to May 2024, with updated analyses performed from August 2024 to April 2025. Exposure Race and ethnicity, behavior factors, health conditions, and social determinants of health measurements accessed before and during the COVID-19 pandemic through cohort visits and C4R questionnaires. Main Outcomes and Measures Self-reported resilience was collected via 1 question (from the Brief Resilience Scale) in C4R questionnaires, “I tend to bounce back quickly after hard times.” Participants who answered agree or strongly agree were classified as resilient, and those who reported neutral, disagree, or strongly disagree were classified as not resilient. Modified Poisson regression was performed to estimate prevalence ratios (PRs) and access multivariable-adjusted associations with resilience. Results Of 31 045 participants (18 672 [60%] women; 10 746 [34.6%] aged <65 years), 1185 (3.8%) identified as American Indian, 6728 (21.7%) as Black, 293 (0.9%) as East Asian, 6311 (20.3%) as Hispanic, 565 (1.8%) as South Asian, and 15 961 (51.3%) as White; a total of 23 103 participants (74.4%) self-identified as resilient. Compared with White participants, Black and Hispanic participants had higher prevalence of self-reported resilience (adjusted PR [aPR], 1.04; 95% CI, 1.02-1.06; aPR, 1.08; 95% CI, 1.06-1.11; respectively) and American Indian and East Asian participants had lower prevalence (aPR, 0.90; 95% CI, 0.86-0.94; aPR, 0.76; 95% CI, 0.68-0.84; respectively). Higher education, being married or living as married, higher income, and overweight were also associated with higher prevalence of resilience. Being female, having diabetes, and being unemployed were associated with lower prevalence of self-reported resilience. Compared with participants with public insurance only, participants with private insurance had higher prevalence of resilience (aPR, 1.07; 95% CI, 1.03-1.10). COVID-19 vaccination and infection statuses were not significantly associated with resilience. Modification analyses showed important racial and ethnic differences in how factors such as hypertension, marital status, and insurance status were associated with resilience. Conclusions and Relevance In this cross-sectional study of 31 045 adults, self-reported resilience varied by race, ethnicity, and sociodemographic factors. These findings highlight the complex interplay of individual and social factors in shaping the perception of resilience.